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Description: CSSW25 proceedings
City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.
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Description: CSSW25 proceedings
City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.

City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.

City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.

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Description: CSSW25 proceedings
City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.
Abstract
The use of Artificial Intelligence (AI) continues to gain momentum in the wastewater industry. With continued development in AI technology, the use of AI in automated defect recognition and coding of sewer lines and manholes could potentially change the way sewer collection systems are managed and maintained in the future. With more than three years into the City of Houston's use of AI in detecting and coding defects in sewer lines, this presentation looks to highlight the challenges and lessons learned. The City of Houston, TX is currently under a consent decree to televise and inspect all the sewer lines in the City over a ten (10) year period, develop a plan to address any major defects found during the inspection and execute the plan. The volume of the lines involved require innovative ways to expedite the inspection and defect coding and subsequently speed up the process of evaluating cost effective and appropriate rehabilitation/repair methods to address the identified major defects. To help achieve this goal, the City of Houston has deployed the use of AI in detecting and coding defects in sewer lines following an evaluation of multiple AI platforms to determine accuracy and consistency of results as well as efficiency (turnaround time). Following the evaluation, one platform was selected for implementation. This presentation presents the challenges and lessons learned from the implementation and use of the selected AI platform over a period of a year and half, and the integration of the results into the City's sewer rehabilitation and management as well as Consent Decree reporting. With the drive for newer, quicker, cheaper and efficient ways to accurately detect and code defects in sewer collection systems, this presentation will provide collection system operators with a quick overview of AI technologies currently available on the market and provide a basis for discussion with their staff on possible use with the management as well as operation and maintenance of their respective sewer systems.
This paper was presented at the WEF/WEAT Collection Systems and Stormwater Conference, July 15-18, 2025.
Presentation time
10:45:00
11:15:00
Session time
08:30:00
11:45:00
SessionInnovations in Texas Water Infrastructure
Session number21
Session locationGeorge R. Brown Convention Center, Houston, Texas, USA
TopicArtificial Intelligence, Asset Management, Collection Systems
TopicArtificial Intelligence, Asset Management, Collection Systems
Author(s)
Bello, Ayobamidele, Satra, Keval, Rabbi, Fazle
Author(s)A. Bello1, K. Satra1, F. Rabbi2
Author affiliation(s)HR Green Inc., 1HR Green Inc., 1City of Houston, 2
SourceProceedings of the Water Environment Federation
Document typeConference Paper
PublisherWater Environment Federation
Print publication date Jul 2025
DOI10.2175/193864718825159825
Volume / Issue
Content sourceCollection Systems and Stormwater Conference
Copyright2025
Word count22

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Description: CSSW25 proceedings
City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.
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Description: CSSW25 proceedings
City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.
Abstract
The use of Artificial Intelligence (AI) continues to gain momentum in the wastewater industry. With continued development in AI technology, the use of AI in automated defect recognition and coding of sewer lines and manholes could potentially change the way sewer collection systems are managed and maintained in the future. With more than three years into the City of Houston's use of AI in detecting and coding defects in sewer lines, this presentation looks to highlight the challenges and lessons learned. The City of Houston, TX is currently under a consent decree to televise and inspect all the sewer lines in the City over a ten (10) year period, develop a plan to address any major defects found during the inspection and execute the plan. The volume of the lines involved require innovative ways to expedite the inspection and defect coding and subsequently speed up the process of evaluating cost effective and appropriate rehabilitation/repair methods to address the identified major defects. To help achieve this goal, the City of Houston has deployed the use of AI in detecting and coding defects in sewer lines following an evaluation of multiple AI platforms to determine accuracy and consistency of results as well as efficiency (turnaround time). Following the evaluation, one platform was selected for implementation. This presentation presents the challenges and lessons learned from the implementation and use of the selected AI platform over a period of a year and half, and the integration of the results into the City's sewer rehabilitation and management as well as Consent Decree reporting. With the drive for newer, quicker, cheaper and efficient ways to accurately detect and code defects in sewer collection systems, this presentation will provide collection system operators with a quick overview of AI technologies currently available on the market and provide a basis for discussion with their staff on possible use with the management as well as operation and maintenance of their respective sewer systems.
This paper was presented at the WEF/WEAT Collection Systems and Stormwater Conference, July 15-18, 2025.
Presentation time
10:45:00
11:15:00
Session time
08:30:00
11:45:00
SessionInnovations in Texas Water Infrastructure
Session number21
Session locationGeorge R. Brown Convention Center, Houston, Texas, USA
TopicArtificial Intelligence, Asset Management, Collection Systems
TopicArtificial Intelligence, Asset Management, Collection Systems
Author(s)
Bello, Ayobamidele, Satra, Keval, Rabbi, Fazle
Author(s)A. Bello1, K. Satra1, F. Rabbi2
Author affiliation(s)HR Green Inc., 1HR Green Inc., 1City of Houston, 2
SourceProceedings of the Water Environment Federation
Document typeConference Paper
PublisherWater Environment Federation
Print publication date Jul 2025
DOI10.2175/193864718825159825
Volume / Issue
Content sourceCollection Systems and Stormwater Conference
Copyright2025
Word count22

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Bello, Ayobamidele. City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned. Water Environment Federation, 2025. Web. 16 Jul. 2025. <https://www.accesswater.org?id=-10117268CITANCHOR>.
Bello, Ayobamidele. City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned. Water Environment Federation, 2025. Accessed July 16, 2025. https://www.accesswater.org/?id=-10117268CITANCHOR.
Bello, Ayobamidele
City of Houston's use of Artificial Intelligence in Automated Defect Recognition and Coding of Sewer Lines, Reflections, Challenges and Lessons Learned.
Access Water
Water Environment Federation
July 18, 2025
July 16, 2025
https://www.accesswater.org/?id=-10117268CITANCHOR